Details
Becoming a Data Scientist is not easy. It involves fundamental knowledge like statistics, data wrangling, to visualization. You will need to understand the techniques to analyze data and communicate the results.
Use your Skillsfuture credit for this boot camp, it's a hands-on guided course for you to learn the concepts, tools, and techniques that you need to begin a data science career. It covers concepts from data science to big data, and the processes of gathering, cleaning and handling data. The course achieves a balance between theory and practical, using case studies as references and instructor-led lab practicals. Upon completion, you will be able to perform basic data handling tasks, collect and analyze data, and present them using industry standard tools.
Dr. Lau Cher Han is a Data Scientist and Full-Stack Developer who base in Melbourne, Australia. With 15 years of experience in database technologies, data mining, and analytics under his belt, Dr. Lau’s background includes problem-solving, apps development and research experience in some of the top-notch tech companies around the region.
Dr. Lau worked as a lead research developer at the Microsoft e-Research Center, where he built a sensor network platform that utilizes mobile phones to record audio signals for detecting rare bird species and koalas. Dr. Lau then joined the Australian Institute
of Future Environments to build a Greenhouse Gas Analysis System, which collects, analyzes and publishes weather and environment data automatically to the cloud. He then co-founded and served as the CTO for JoinHappen.com, where he designed the technology
blueprints and drove product development. Dr. Lau also provides training to universities, as well as various industry corporations such as Fusionex, HP, and Intel, to upskill their staff with up-to-date technologies.
- Identify appropriate model for different data types.
- Create your own data process and analysis workflow.
- Differentiate key data ETL process, from cleaning, processing to visualization.
- Implement algorithms to extract information from dataset.s.
- Apply best practices in data science, and familiar with standard tools..
Outline
Introduction to Data Science
- What is Data?
- Qualitative vs Quantitative Data
- What is Data Science?
- Statistical thinking
- Lab Practical
Data Processes
- Perform Extract, Transform and Load (ETL)
- Data cleansing
- Aggregation, Filtering, Sorting, Joining
- Data workflow
- Lab Practical
Data Science Workflow
- Asking the right question and identifying problem
- Defining question and hypothesis
- What is an ideal dataset
- Where to obtain data?
- Analyse data and interpret results
- Lab Practical
Database Design
- Types of databases
- Relational databases
- Non-Relational databases (NoSQL)
- Hybrid data architecture
- Lab Practical
Structured Query Language (SQL)
- CRUD (Create, Retrieve, Update, Delete) operations
- Designing a real world database
- Table normalization
- Lab Practical
Introduction to Python
- Basics of Python language
- Python lists and objects
- Modules and packages
- Functional programming in Python
- Using scientific packages Pandas, Numpy and Scipy
- Integrate Jupyter Notebook in data science workflow
- Lab Practical
Advanced Data Handling
- Obtain data from online repositories
- Import data from local file formats (json, xml)
- Import data using Web API
- Scrape website for data
- Knowledge check
- Lab Practical
Exploratory Data Analysis
- What is EDA?
- Goals of EDA
- The role of graphics
- Handling outliers
- Dimension reduction
Introduction to R
- Features of R
- Vectors
- Matrices and Arrays
- Data Frame
- Input / Output
Final Mini Data Science Project
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